Fuzzy logic control of an RF power amplifier for automatic self-tuning

ABSTRACT

Fuzzy logic is utilized to control an RF amplifier and associated tuner for continuous self-optimization and automatic load matching to at least double the battery life of a battery-powered transmitter.

RELATED APPLICATIONS

This Application claims rights under 35 USC §119(e) from U.S.application Ser. No. 60/850,769 filed Oct. 11, 2006, the contents ofwhich are incorporated herein by reference.

STATEMENT OF GOVERNMENT INTEREST

This invention was made with United States Government support underContract No. DAAB07-02-C-P-632, awarded by the Department of the Army.The United States Government has certain rights in the application.

FIELD OF THE INVENTION

This invention relates to conservation of battery power andbattery-powered RF transmitters and more particularly to the utilizationof fuzzy logic control of an RF power amplifier and associated tuner forcontinuous self-optimization and automatic load matching, thereby todouble the battery life of the battery-powered device.

BACKGROUND OF THE INVENTION

Portable transceivers that include both commercial transceivers such ascell phones and military radios have been battery powered, presentlyutilizing lithium battery technology to increase the time in which thebattery-powered RF device can operate.

Cell phones, two-way radios, Bluetooth devices, and any number ofwireless communications units having power amplifiers draw battery powerbased on the function of the device.

Heretofore there has been no self-adaptive configuration of the RFamplifiers as to, for instance, sense the environment and adapt thetuning associated with the final RF amplifier so that the amplifier isoperating as efficiently as possible. It is of course desirable that theamplifier operate in a very linear operating region in terms oftemperature and to minimize the standing wave ratio (SWR). Thus, forinstance, when an individual using a handheld wireless device stands ina doorway that has a metal frame, the metal frame can detune the antennaor cause a significant mismatch in the impedance between the antennainput and the output of the RF amplifier.

Typically these wireless units are engineered without consideration ofthe changing environmental conditions in which they will operate; and asa result do not operate as efficiently as possible. The result is unduebattery drain when the environmental conditions change at the portabledevice.

As to cell phones, power management capability is typically managed bythe base station in which the power output is determined from the basestation. In these cases there is no local tuning or other control of thecell phone RF amplifiers, which is handled externally and notintelligently by the device itself. As will be appreciated, the basestation does not take into account any environmental factors such astemperature and changing SWR. Thus it is not the function of the basestation to make any adjustments that would limit current drain so thatthe portable unit does not to run out of power.

While in the past RF transmitters have been provided with automaticantenna tuners, they are large and expensive and while they can measurestanding wave ratios, they are not utilized in small handheld low-costsystems. Also the response time of the commercially available automaticantenna tuners oftentimes exceeds several seconds, which is notparticularly effective in instantaneously optimizing the poweramplifier/antenna portion of the transceiver.

Moreover, the algorithms utilized in automatic antenna tuners can takeas many as 100,000 instructions. These sophisticated algorithms requirelarge processors with the attendant latency and massive currentconsumption. Thus there is always a requirement that one conservebattery power to extend missions, and one cannot conserve battery powerutilizing large processors.

There is therefore a need to optimize the RF transmitting section of atransceiver or wireless device so as not to unnecessarily impact batteryresources.

What is desirable is to be able to sense the operating conditions at theamplifier and to have a feedback path by which the amplifier and itstuning circuits can be adjusted so as to optimize the RF amplifier asits operation is altered or degraded by the environmental conditions inwhich it finds itself. Additionally, it would be useful to be able tooptimize the RF amplifier and its attendant circuitry in terms of anydegradation due to circuit aging so that the RF amplifier portion of theportable device self-adjusts to minimize current drain, thus to extendbattery life.

In short, there is a requirement to automatically match an RF poweramplifier to a load and to ensure that optimized power is delivered to aload whilst the RF amplifier or connection to the antenna degrades dueto environmental conditions.

SUMMARY OF INVENTION

In order to optimize RF power amplifiers for use in battery-powereddevices, in accordance with the subject invention, fuzzy logicalgorithms are used in a closed loop feedback circuit to applycontinuous monitoring and self-correction fine-tuning for the RF poweramplifier. This maintains the amplifier in a linear region. Ifsubstantial impedance mismatches occur between the amplifier and theantenna, the amplifier operation goes non-linear and can even oscillate,drawing excessive power from the battery and severely limiting batterylife. The subject system is useful not only for narrow-band cellularsystems but also for WiFi 802.11 transceivers, spread spectrum systemsand ultra wideband communications where the frequency rapidly shifts andthe antenna tuning must follow.

It has been found that by utilizing fuzzy logic, no more than 1,000instructions and oftentimes as few as 100 instructions need be used,which minimizes controller power requirements and also limits theoptimizing cycle to microseconds.

In one embodiment the fuzzy logic algorithms are programmed on a singleintegrated circuit due to their simplicity, such that standard low-costEPROM technology may be utilized. This eliminates the utilization ofsophisticated digital signal processors often entailing Pentiumprocessors as well as the massive amount of memory that is required.

In one embodiment, output power and input frequency are sensed and adigital tuner is re-tuned, with the fuzzy logic algorithms controllingthe amplifier and the tuner to maintain an impedance match between theamplifier and the antenna and to adjust amplifier gain. By monitoringoutput power and comparing it to an idealized output power, when theoutput power changes, adjustments can be quickly made.

It is noted that with respect to cell phones, there is nothing at thecellular telephone that monitors environmental conditions, much less anyfeedback network to optimize the cellular phone handset operation.

As part of the subject invention, not only is the tuning of theamplifier monitored by monitoring output power, one also can adjust thebias of the amplifier, which changes the gain of the amplifier. If thereis a substantial mismatch between the output impedance of the amplifierand the input impedance of the antenna, then by adjusting down the gainof the amplifier the standing wave ratio mismatch can be minimized.

As mentioned above, the use of fuzzy logic is critical to the rapid andlow current drain operation of the subject system. It is noted that dueto the limited number of instructions necessary in a fuzzy logicalgorithm, one can place the algorithm on a single chip without the useof external components. If one has 100 instructions to tune theassociated RF amplifier and the clock rate of the microcontrollerexecuting the instruction is 1 MHz, even with a slow processor one canuse the 100 instructions to do an overall tuning operation in themillisecond range. Thus one can use an extremely slow computer chipembedded controller running at 1 MHz. In summary, with only 100 to 1,000instructions, even with a 1 MHz clock one achieves millisecond cycletimes.

The primary motivation for using fuzzy logic is that it does not have tohave an absolute deterministic value. For instance a rule table can beset up by an expert that specifies that if certain sensed parameters arein a certain range, change the tuner state to a predetermined state thatresults in a better match. Fuzzy logic thus deals in ranges as opposedto absolute values. Moreover, fuzzy logic, because it groups data intoranges, operates on a much-reduced data set, with the fuzzy logic makinga decision when the result using this reduced data set is good enough orclose enough. Thus if the result is between 90 and 95% close to theideal, then this type of operation is sufficient, at least in thesubject case to save as much as five times on the battery drain byoptimizing the RF amplifier/antenna circuit.

While military radios have been designed for optimized battery life, upto the present time there has been no attention to optimizing the RFsection of the transceiver or transmitter on the fly. While at thedesign phase of these wireless devices optimization techniques are used,environmental degradation can cause a significant mismatch anddegradation of the operation of the amplifier so that it operatesoutside of its linear region. If one were able to re-tune the amplifierand/or set its gain parameters appropriately, one could multiply thebattery life by at least two times and as much as five times.

The external factors affecting the transmit scenario relate in part tothe problem of an antenna being placed next to a metal structure, insideof a building or even out in the open. Environmentally induced detuningcan result in antenna gain changes. For instance, if an antenna isoptimized for a 3 dB gain in an ideal environment, if suddenly a soldierstands under a metal door frame and is talking on the wireless device,the antenna now, for instance, may be one inch away from the metalframe. Thus the antenna gain may go down to 2 dB or may even go to zero.A drop of 1 dB can cost at least half the power. Since portable wirelessdevices are meant to be moved around, non-optimal operation, ifdetected, can result in the opportunity to optimize of the RFamplification section of the transceiver.

The use of fuzzy logic algorithms provides an architecture that isexpandable. For instance, while in the subject example one senses outputpower and input frequency, one could provide the fuzzy logic with anynumber of different sensors. One could be monitoring temperature as wellas the lifetime of the device in addition to power and frequency. Forinstance, if one knows the slope of the amplifier changes, one couldoptimize the amplifier to counteract these changes.

In fact, fuzzy logic is so simple that by just adding ten lines of codeone can monitor an additional sensor and include the result in thefeedback network.

The result is that low-cost radios can be placed in the hands ofsoldiers in a platoon where the design goal would be a 24- to 48-hourmission. For instance, if the soldiers are going out on a convoy, onewould like to have the battery outlast the planned mission.

In one embodiment of the subject invention, sensed parameters are usedby the fuzzy logic to control an antenna tuner with an initial setupresulting in a set of ideal impedance points on the Smith Chartdescribing an acceptable 1:1 SWR antenna match. Thereafter new sensedparameters result in different impedance points on the Smith Chart. Thedistance between the new Smith Chart points and those in the ideal setis ascertained. The fuzzy logic through its rules and based on thiserror signal generates a digital tuner control signal to reconfigure thetuner to reduce the error. In a next iteration, new sensed parametersresult in a new error signal used to control the amplifier tuner. Thisresults in a new Smith Chart position. If this new Smith Chart positionis sufficiently close to the ideal position, the process is stopped. Ifnot, a fine-tuning algorithm is invoked.

When the resulting Smith Chart position is close enough to an idealizedset of Smith Chart positions, the amplifier operates in a linear region.If the SWR becomes too high the amplifier can operate in a non-linearand even oscillating region, which draws excessive power.

If the transceiver is a frequency- or band-hopping radio, keeping theSWR under control is a problem, especially for spread spectrum militaryradios or ultra wideband devices. While cell phones are narrow-banddevices, WiFi transmitters can also operate in wideband. In all of thesecases maintaining the RF final amplifiers in a linear range isproblematic.

Thus in the subject system, after initialization, a Smith Chart positionis detected based on measured data. The fuzzy logic algorithm thenestablishes what actions are necessary to drive the Smith chart positionto the area or region of the Smith chart that is close to the desired1:1 SWR.

For instance, if the antenna is designed to have a 50-Ohm inputimpedance and the output of the amplifier is designed to have an outputimpedance of 50 Ohms, then there is a position or area on the Smithchart that corresponds to this.

When environmental factors change the antenna input impedance or the RFamplifier output impedance, then these conditions are sensed and theposition on the resulting Smith Chart position is detected. The distancebetween the point determined by these conditions and the optimal 1:1 SWRpoint on the Smith chart is detected and feedback signals are applied toeither the tuning apparatus for the amplifier or to control the gain ofthe amplifier to drive this point towards the desired 1:1 SWR point onthe Smith chart.

It has been found that only three such tuning cycles are needed in orderto place the RF amplifier/antenna circuit at a relatively optimal point,which can be done with as few as 100 lines of code and in milliseconds.

In order to do so, the fuzzy logic sets are set up using a series ofdefinitions called membership functions to which variables are assigned.The membership functions are then put in the fuzzy logic sets which arestored in memory.

When the radio is turned on these sets are pulled out of memory and areloaded to set up the tuner. Thereafter, the fuzzy logic algorithm is runto initialize the system, which means selecting initial tuner states.

Thereafter, power and frequency measurements are made to ascertainoutput power error, the magnitude of which forms an error signal.

As a result of the three iterations in the above example, one has tunedthe amplifier/tuner circuit to its optimum point, assuring amplifierlinearity.

If after the first three iterations as mentioned above there is nosatisfactory movement of the Smith chart point, then one drops into afine-tune mode. In order to prevent oscillation or non-convergence inthe fine-tune mode, the number of tuning steps in the fine-tune mode islimited to a maximum number of iterations.

Put another way, given a particular tuner, the tuner can take on manydifferent tuning states that result in various points on the SmithChart. Taking, for instance, ten tuning states, this means the tuner cantake on ten different configurations. It is the purpose of the fuzzylogic algorithm to pick the tuning state that makes the amplifieroperate in a linear region. How the algorithm does this is as follows:

Initially one establishes an initial tuning state that is the ideal orbenchmark that provides linear amplifier performance, i.e., correspondsto a 1:1 SWR. One then has in the above example nine other tuningstates, which gives nine different Smith Chart points or results. Thedistance from each of these points to the ideal point is described by aweight that is assigned to the corresponding tuning state.

In one embodiment, the microprocessor is pre-loaded with the ten tuningstates and their corresponding weights.

The initial ideal tuning state corresponds to a 1:1 SWR for a givenfrequency input to the RF power amplifier.

Then one turns on the transceiver and measures the output power andinput frequency. From the absolute magnitudes of these two measurementsthe distance of the corresponding Smith Chart tuning state point to theideal point is determined in terms of a weight. The fuzzy logic, usingweights, then computes a tuning state that will result in a Smith Chartpoint closer to the ideal point; and the tuner is set accordingly.

After selecting this new tuning state, the transceiver potentiallyoutputs a different output power and/or different frequency. Thedifferent power and different frequency are compared against theinitialized values to derive another error signal corresponding to aweight which is used to pick a new tuning state.

The magnitude of the error signal is used to pick a tuning state thatwill result in a decrease in the distance between the point on the SmithChart previously obtained and the ideal point.

In essence the fuzzy logic is utilized to pick the tuning state thatresults in a Smith Chart point that is closer to the ideal Smith Charttuning state point.

Instead of having only ten tuning states, which are not enough to coverthe Smith Chart, in one embodiment the tuner is designed to have 2,048tuning states, which results in enough points to cover the Smith Chart.

However, fuzzy logic cannot efficiently handle 2,048 tuning states. Whatis required is a reduced data set and this is accomplished by groupingthese 2,048 tuning states in one embodiment into five sets to be able tomore quickly handle the data.

However, fuzzy logic efficiently handles 2,048 tuning states by groupinglarge amounts of data into sets. This reduced data set is accomplishedby grouping these 2,048 tuning states in one embodiment into five setsfor the fuzzy logic algorithms to be able to more quickly process thedata.

Thus for five sets there are approximately 400 different tuning statesin a set, each with a different weight.

How the sets are formed will be described hereinafter. Note that eachset is designed so that tuning states that have similar characteristicsare grouped in a set. In setting up the sets an expert designates fiverepresentative tuning states and thus five weights per set. Theserepresentative tuning states may be selected to correspond to SmithChart points at the center, up, down, left and right in the Smith Chartarea corresponding to the set.

Thereafter and within each set there is a root mean square averageweight for the five selected weights for the set. This root mean squareaverage is compared to the magnitude of the error signal, and the tuningstate corresponding to the root mean square average that results in acloser Smith Chart point is selected for controlling the tuner.

Thus the root mean square average of five representative weights is usedto make a decision as to how to tune the tuner.

By way of example, in order to specify the tuning states, a technicianwould take a given tuner and measure the Smith Chart point for each ofthe tuning states, with the results loaded in memory.

How the tuning states are grouped into sets is basically determined byan expert who must know fuzzy logic, at least to make a rough good setof data specifying each of the sets so that like tuning states aregrouped together in a set.

One way to do this is as follows:

Assuming a digitally controlled tuner, for instance having 8-bitcontrol, the technician could designate 0, 0, 0, 0, 0, 0, 0, 0 as being“Tuning State 1.” “Tuning State 2” might be 0, 0, 0, 0, 0, 0, 0, 1. Thetechnician would then sequence from “Tuning State 2” up to “Tuning State400” and designate this as the first set.

While this may sound arbitrary, because one is using a particulardigitally controlled tuner, by selecting sequential numbers oneidentifies tuner states having closely similar characteristics.

The above process corresponds to coarse tuner control; and the resultafter these iterations may be close enough to let the tuner remainunchanged.

It has been found that in the above example it takes only threeiterations to select a tuner state to obtain close to a 1:1 SWR so thatthe amplifier operates in its linear region. This in turn minimizesbattery drain and can extend battery life five-fold.

While the above has described the tuner control, it is also possiblewith the above fuzzy logic to control amplifier biasing and thus gain,again to minimize SWR. Also a number of parameters other than inputfrequency and output power such as temperature and circuit aging can beused to maintain linear operation of the amplifier.

In summary, fuzzy logic is utilized to control an RF amplifier andassociated tuner for continuous self-optimization and automatic loadmatching to at least double the battery life of a battery-poweredtransmitter.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of the subject invention will be betterunderstood in connection with the Detailed Description, in conjunctionwith the Drawings, of which:

FIG. 1 is a block diagram of the subject fuzzy logic control system forthe control of a tuner coupled to an amplifier in which input frequencyand output power is monitored for the control of the tuner and in whichinput power is monitored for gain control of the amplifier;

FIG. 2 is a diagrammatic illustration of amplifier response on a SmithChart, the possible tuner states for a given tuner, in one embodiment adouble-stub tuner, and the generation of a number of tuning state sets,the average weight of which is to be compared with the average weight ofan ideal tuning state set indicating an acceptable match between theamplifier and tuner of FIG. 1;

FIG. 3 is a diagrammatic illustration of the impedance-matchingalgorithm containing the fuzzy logic control for the subject system;

FIG. 4 is an impedance tuner algorithm block diagram illustrating thedetection of output power and input frequency, the derivation of errorsignals and the implementation of a coarse tune algorithm for providingweight vectors to set the tuner of FIG. 1 by generating appropriatedigital commands;

FIG. 5 is a flow chart for the initialization of the subject system;

FIG. 6 is a diagrammatic illustration of an overlapping Smith Chart forimpedance transformation indicating a set of impedance points for anideal set corresponding to the most optimum tune for the tuner of FIG.1;

FIG. 7 is a flow chart indicating a digital feed forward algorithm thatuses fuzzy logic to control the digital tuner in FIG. 1, as well asmonitoring and adjusting gain of the amplifier of FIG. 1; and,

FIG. 8 is a decision table showing fuzzy logic rules for setting thetuner states for the tuner of FIG. 1.

DETAILED DESCRIPTION

Referring now to FIG. 1, an RF power amplifier 10 is coupled to asuitable tuner 12 which in one embodiment for microwave frequencies is adouble-stub tuner that is digitally controlled. An output power sensor14 is located adjacent the RF output 16 of tuner 12 to provide a measureof the output power that will be delivered to an antenna 18.

Also provided as an input to the subject system is a frequencydiscriminator 20 that measures the frequency of an RF input signal at RFinput 22.

These two measured quantities, namely the input frequency and the outputpower, are coupled to a fuzzy logic control circuit 24, which providesfor digital control of tuner 12 over line 26.

Additionally, RF input power is measured at 28 and is applied to a gaincontrol algorithm in unit 30 that controls the bias for power amplifier10.

While a separate control loop may be provided for gain control of the RFamplifier, in a further embodiment of the subject invention amplifiergain may be controlled by fuzzy logic control circuit 24 operating underan appropriate rule set to lower the amplifier gain in the presence of alarge SWR mismatch between the amplifier and the antenna. This controlis indicated by reference character 31 on a line between the controlcircuit and the amplifier.

In short, one has analog circuits 32 that provide a signal to RF inputterminal 22, with the fuzzy logic control algorithm in control circuit24 providing intelligent control for auto-tuning to be able to match theamplifier output impedance to the antenna input impedance.

Referring now to FIG. 2, as described in combined Smith Chart 40, thecombined amplifier/tuner response that experts say will provide the mostlinear operation of the amplifier is shown at 42 to be positioned, forinstance, slightly above the 50-Ohm impedance 44 and slightly to theright of dead center.

In one embodiment the digitally controlled double-stub tuner has 2,048states, with the corresponding impedance points of the tuner statesshown in Smith Chart 46. Thus, for each frequency there are 2,048possible tuner states, with the pattern on Smith Chart 46 varyingdepending on the input frequency.

As mentioned hereinbefore, fuzzy logic eschews the utilization of somuch data and the 2,048 possible tuner states are divided up into fivesets. The ideal set, here shown at 50, is that which results in aminimum impedance mismatch between the amplifier and the antenna ofFIG. 1. This set is labeled Set 5 in the FIG. 2 diagram.

Other tuning sets 52 are as illustrated and contain impedance pointsthat are the result of closely related tuner states.

Referring specifically to Set Number 4, it will be appreciated that eachof the impedance points is given a weight depending on how far the pointis to Set 5. This weight is a vector having both a magnitude and adirection.

Referring to impedance point 54 in Set 4, its distance and direction toSet 5 in accordance with its assigned weight will be larger or “morebad,” whereas for impedance point 56, the distance and thus the weightassigned to this point is “more good.”

Due to the use of fuzzy logic, with impedance points grouped in sets asindicated above, the average point in a set is determined by the rootmean square average of all the points in the set. It is from thisaverage point that distance to the ideal set is measured.

As will be discussed, the measurement of output power and inputfrequency relative to an initialized set of values allows one to derivea weight vector such that having derived the weight vector, one cangenerate a digital control signal applied to the tuner to tune the tunerto a point that will result in an impedance point on the Smith Chartthat is closer to the ideal set.

Rather than processing data for 2,048 possible double-stub tuner states,one infers from the measured data how to go from a point within anoutlying set to the ideal set. The weight for so doing is converted intoa tuner state that minimizes the weight.

All of this is accomplished through a fuzzy logic control systemimpedance-matching algorithm. In this algorithm, sensor inputs arereceived by the system and evaluated. The antecedent (if x and y) blockstest the inputs and produce conclusions. The subsequent (then z) blocksof some rules are satisfied while others are not. The conclusions arecombined to form logical sums. The conclusions are then fed into theinference process where each response output member function's firingstrength (0 to 1) is determined.

How the impedance-matching algorithm operates is now discussed.

Prior to discussing the operation of the system described in FIG. 4, theway that the fuzzy logic operates is that it works in sets. In the mostgeneral sense, one set is the universe. It is all of the possibilitiesthat one could have for the tuner.

Then as described above there is an ideal tuning solution in the form ofan ideal set. The way that the fuzzy logic works is by making a decisionas it tries to solve how far away one is from where one wants to go,namely the ideal tuning set.

The way the system works is to make a measurement of how far the senseddata is from the optimum data. In a series of measurements one knowswhen the weight derived is closer or not so close to the weightsassociated with the ideal set.

If the algorithm taking the measured data knows that the weight is toogreat, then for the next iteration the tuning must go in a differentdirection to minimize the magnitude of the error signal.

The Algorithm Theory of Operation

The equation in FIG. 3, which maps the system input to input, isspecified by:

$\frac{\sum\limits_{p = 1}^{\;}{y_{p}{\prod\limits_{i = 1}^{g}\;{\alpha\;{aip}}}}}{\sum\limits_{p = 1_{p}}^{m}{\prod\limits_{i = 1}^{g}\;{\alpha\;{aip}}}}$where m is the total number of rules, y is the crisp output for eachrule, αaip is the product of the membership functions of each ruleinput, and g is the total number of inputs. In the first iteration ofthe intelligent transmitter, g will be defined to be the three sensorinputs (frequency in, power input, and power output). The finalimplementation of this algorithm may have a g with as many as fiveinputs (power in, frequency in, power out, temperature, and waveformdiscrimination). The membership functions are mapped according to inputsand to outputs. The input membership functions are a range of sensorparameters for each g, as is disclosed in H. Hagras, V. Callaghan, M.Colley, “Developing an outdoor fuzzy logic controlled agriculturalvehicle for crop following and harvesting,” the contents of which areincorporated herein by reference.

In the intelligent transmitter, two inputs (actual power and actualfrequency) are continuously monitored and compared to the desired poweroutput level and a reference frequency. The evaluation of these twoinputs establish error signals. The magnitude of each error signal isbased upon the logical rule set established in the algorithm. Forexample, if the amount to tune is small, then the magnitude of the erroris small. Conversely, if the amount to tune is large, then a large errorsignal will be generated. The logical evaluation of the inputs and thegeneration of the error signal or signals is executed by a Booleansuperset called fuzzy logic. Basically, the fuzzy logic evaluates allsensor inputs that are received by the system concurrently. Theantecedent (If X and &) blocks test the inputs and produce conclusions.The subsequent (Then Z) blocks of some rules are satisfied while othersare not. The conclusions are combined to form logical sums. Therefore,the algorithm constantly monitors the system and always makes a decisionto tune or not. Implemented in code, this results in very fastadaptation times.

The fuzzy logic algorithm uses fuzzy reasoning to model the systemcharacteristics. Lotfi A. Zadeh introduced the concept of fuzzy logic in1965, as is disclosed in L. Zadeh, “Fuzzy Sets,” Informat. Conf. Vol. 8,pp. 338-353, 1965, the contents of which are incorporated herein byreference. This work emphasized that humans are better at control thanconventional controllers because they make effective decisions on thebasis of imprecise linguistic information. His proposition was using aBoolean superset, i.e., a degree of possibilities in between a logicaltrue and a logical false, that he termed fuzzy logic. Applied to thecontrol of various systems, fuzzy logic describes the system behaviorbased upon “our” knowledge of the system. Complex or simplerelationships between system variables are characterized no matter whattheir analytical dependence. This is performed by a rule set in the formof “IF a set of conditions is satisfied, then a set of conclusions isinferred, as disclosed in P. Branco, J. Dente, “An experiment inautomatic modeling an electrical drive system using fuzzy logic,” IEEETransactions on Systems, Man and Cybernetics, Vol. 28, Part C, No. 2,May 1998, the contents of which are incorporated herein by reference.This is depicted in the following equation:R ^((l)) : lfx _(i) is A ₁ ^((l)) and x ₂ is A ₂ ^((l)) . . . and x _(m)is A _(m) ^((l)) then y is B ^((l))

The symbol R^((l)) (1≦l≦c) corresponds to the lth model rule among atotal of c rules, xj (1≦j≦m) is the m chosen system variable expressingthe system condition, y is the system output variable, and

is the inferred value from the fuzzy model.

The conclusions of the fuzzy logic evaluation are fed into the inferenceprocess, where each response output member function's firing strength ortruth level (0 to 1) is determined. This process by which the inferenceengine computes this uses the error signal magnitude to select a tuningrange. In other words, the membership functions will contain theoptimized tune for the system, and it is the job of the inference engineto make the tuning decisions. The equation for the membership functionis as follows:μ_(A1) ^((l)) _(x . . . xAm) ^((l))(x1, . . . ,xm)=μ_(A1) ^((l))(x ₁)+ .. . +μ_(Am) ^((l))(x _(m))In each rule, A_(j)(1) is the fuzzy set (linguistic term) and ischaracterized by a membership function:μ_(A1) ^((l))(x _(j))Finally, the fuzzy set B⁽¹⁾ represents the conclusion part of the rule.

A weighting system is applied to the membership functions in order toassign a ratio of true and false association to each decision. Forexample, if the decision is arrived at that is exactly between twotuning ranges, then one weight factor assignment could be 50% true or50% false. However, the system is trained such that previous lessonslearned indicate that a 70% true and a 30% false weight is moredesirable. This inference process is depicted as the following:

$Y = \frac{\sum\limits_{1 = 1}^{c}{{\mu\left( R^{(1)} \right)}\omega^{(1)}}}{\sum\limits_{1 = 1}^{c}{\mu\left( R^{(b)} \right)}}$Y depicts the inferred model output, i.e., the optimized tuningsolution, ω⁽¹⁾ is the logical evaluation result of each rule, μ(R⁽¹⁾) isthe activation rule degree (the weighting factors), and c is the numberof rules after domain partition. Once a very narrow range of solutionsis determined the final decision is then mathematically derived usingseveral different algorithms: root-sum-square, center-of-gravity, andMIN-MAX.

Application of the Expert Inference System to the IntelligentTransmitter

This section will describe the application of the algorithm forautonomous control of the amplifier/tuner circuit. Referring now to FIG.4, this figure shows the architecture of the amplifier control systemcalled herein the intelligent transmitter.

As described earlier, the inputs to the fuzzy controller, here shown at70, are input frequency 72 and output power 74.

A power input sensor is used to adjust gain to keep the input to thepower amp in the optimum range. Furthermore, both the power outputsensor and the frequency input sensor are monitored and used to adjustthe impedance matching network for best power added efficiency (PAE).

Referring now to FIG. 4, the actual output power is measured as 74,whereas the actual input frequency is measured at 72.

The desired output power is illustrated at 76 and it is to this desiredoutput power that the actual detected power is compared. These twopowers are placed into a summing junction 78, which in accordance withthe rules m 80 provides an output power error signal on line 82.

Likewise, the actual input frequency at 72 is compared to a referencefrequency 82, with these two signals provided to a summing junction 84,again provided with rules to provide a frequency error signal applied toline 86. These two error signals are applied to a coarse tune unit 70comprised of Degree-of-fuzziness Membership Unit 88 and Input Weightunit 90, the combined outputs of which at 92 determine a weight vectorthat is converted into a digital tuner tuning signal.

While the subject system in essence utilizes two tuning steps, namely acoarse tuning step and a fine tuning step, what is described now is thecoarse tune in which the tuning states are grouped into sets, dependingon what the error is. The coarse tuning apparatus goes through a processto see if the error gets better or worse and if the result is within thedesired range, then that signal from element 94 is used in the tuningprocess.

It will be appreciated that the coarse tuning section of the subjectsystem is composed of Degree-of-fuzziness Membership Block 88 and InputWeights Block 90, with process respectively the output power errorsignal and the input frequency error signal.

With respect to the Degree-of-fuzziness Membership Block 88, the powererror signal and the frequency error signal are coupled to respectiveblocks 100 and 101, the function of which is to generate a resultcorresponding to the root mean square of the error signal coupled at itsinput. The root mean square function's purpose is to ascertain how farone is from the desired power or frequency. Once the Degree-of-fuzzinessMembership calculation is made, its output signal is applied to theappropriate P factor block, respectively 102 and 103 in Input WeightsBlock 90, the purpose of which is to generate a weight. The weight isused to define what tuning state the tuner of FIG. 1 is to take on.

Thus coarse tune unit 70, using fuzzy logic, ascertains how far thesensed values of power and frequency are from the ideal values thatwould result in a 1:1 SWR and generates a weight having both a magnitudeand a direction. The sum of these weights constitutes an output 105 thatis used to generate a digital code for the control of the tuner of FIG.1.

Since the system operates on root mean square average to describe thedistance between a set of tuning states to a set corresponding to theideal tuning state, this weight is used to feed back a signal to re-tunethe tuner.

In short, 1/max PE is an error ratio, with the magnitude of the ratiodetermining whether the present setting of the tuner is “good” or “bad.”If it is “good,” one proceeds to the final tuning step. If not, thesystem performs another iteration.

With the coarse tuning one wishes to start with an impedance point thatis a large distance away from the ideal impedance point. So onepurposely picks an impedance point that is further away to start outwith.

What is done by the P factor blocks 102 and 103 is as follows. Havingdetermined from blocks 100 and 101 whether or not the ratio is bad orgood, these blocks calculate weight vectors, with these weights beingused to derive digital control signals to re-tune the tuner.

Note that g is the total number of inputs, m is a rule and α is theproduct. Y, here shown at 105, is the output weight that is used todefine the digital signal to the tuner.

The rules m established at 80 are arbitrary. What one does is to pick apoint on the Smith Chart that is far away, that is to say at the extremeside of the Smith Chart. The rule m then says to pick a point on theSmith Chart which could be, for instance, halfway to the ideal SmithChart value. Thus one rule would be to always pick as a first step ahalfway point or halfway weight between the measured point and the idealpoint.

One could arbitrarily choose that the first point be in the capacitiveregion on the Smith Chart or one could also pick a point that is in theinductive region.

Initialization Procedure

Referring now to FIG. 5 and the initialization procedure, to start andas illustrated at 120, one powers up the transmitter at 122, from whichthe gain of the amplifier is adjusted at 124 and the tuner is adjustedat 126. These are adjusted utilizing a knowledge base 130 with all ofthe information merged at 132 and placed into an initializer 134, whichconsists of a table or tables with different setup conditions.

After having set up the gain for the amplifier and the tuner state, oneapplies a test tone at 136 to the amplifier and measures the outputpower from the output power measuring point as illustrated at 138. Theoutput power is compared to the initialization or ideal output power asillustrated at 140 and if the output power matches the initial outputpower specified as illustrated at 142, one moves into the Operation Modeas illustrated at 144.

If not, another iteration or fine tune is performed at 146, with ameasurement then seeking to ascertain if, after the fine tuning, theoutput power matches the initialization output power as illustrated at148. If so, one enters the Operation Mode 144. If not, one makes adetermination at 150 as to whether the initialized value of P-out isgood enough. If so, the system is updated at 152, with the knowledgebase 154 likewise being updated to indicate what parameters areconsidered sufficiently good. A status report is generated at 156 andone again enters the Operation Mode 144. If the output power after finalfine tuning is not good enough, then the process is stopped asillustrated at 158 and an error report is generated as illustrated at160.

What is accomplished by the aforementioned initialization is to set upinitial conditions for the particular amplifier and the particular tunerso that the linearity of the amplifier and/or the mismatch between theamplifier and the antenna can be measured and an appropriate errorsignal generated as described in FIG. 4.

More particularly, in order to establish the ability to learn or toadapt to new operating parameters, the following premise is establishedto enable a learning capability. The system must be initialized uponpower-up and put into known states for starting conditions. In otherwords, how can the system learn if it does not know what to base newinformation on as a point of comparison? Part of the learning experiencehas to include some sort of memory function, to assess changes overtime, or perhaps to predict a degradation of performance. Thedetermination of these states is adaptable based upon knowledge gained(or learned) during operation. The system employs a mechanism to mergethe knowledge gained in operation to the initialization settings.Therefore, the system has memory for future functionality anddecision-making. The power-up initialization uses calibration to verifyoperational parameters. The frequency in one embodiment is normalized at12 GHz for the sole purpose of extracting logic rules for the system,with maximum output power retained in the knowledge base of the system.The actual maximum power out will be ascertained if any changes occur inthe system.

As mentioned above, the flow chart begins with power-up, which meansthat only system power is applied, but no input signal. The startinggain adjustments are fetched from an “Initial Settings” function andapplied to a segmented gate amplifier (SGA) for the known magnitude andfrequency of the test tone. Concurrently, the impedance transformer isalso tuned to its initial settings from the same function. The test toneis applied and P_(out) is measured. P_(out) is then compared to P_(init)(initialization setting) and if they are equal, the system is ready foroperational mode. However, if P_(out)≠P_(init), then the system entersinto the fuzzy logic control algorithm to fine tune. At this point againP_(out) is measured. If P_(out)=P_(init) based upon this new tune, thenthe system is ready for operational mode and the new tuning parametersare input in to the knowledge file. In one embodiment, the new tuningparameters are time stamped in order for the knowledge function to keeptrack of system changes over time. If, however, P_(out)≠P_(init), thenthe system could not reach its previous optimized tune and this meansthat some circuit level parameters have changed drastically. Then thesystem is at a critical junction. “Is P_(out) good enough” will bedetermined by a preset in the initialization file. If it is deemed thatP_(out) is indeed good enough, then the system enters operation mode andsubsequently the knowledge file is updated and as an option, a statusreport is issued to the system.

Intelligent Transmitter Operational Mode

As discussed previously, the intelligent transmitter uses anexpert-based knowledge system with a fuzzy logic kernel controlalgorithm. The control of the transmitter is rule-based, meaning thatthe control is based upon an expert's knowledge of how to tune thesystem. The rules can be quite comprehensive to render expert control ofthe system to a high degree of accuracy. As a general tenet, rule-basedsystems are structured, systematic, repeatable, predictable, and codeefficient. Therefore, if indeed the algorithm were based upon ourknowledge of the system, then the question is begged, how would a humantune the subject transceiver? FIG. 6 illustrates the tuning issues.

The Smith Chart response in one embodiment is shown for a non-uniformdistributed power amplifier, NDPA, which is the power amplifier in thetransmitter. The response is slightly inductive. As a point ofdiscussion, a look-up table could be employed containing the complexconjugate for every point of the power output. However, there areseveral drawbacks with a look-up table. First of all, for the 11-bittuner, there would be 2048 states. Computation time required to compareall the states would be significant, slowing the response down. Further,2048 states imply a one-dimensional or a one-input system. To evaluatemore than one input requires an n-dimensional table, resulting inn-factor more states to evaluate. Perhaps the most compelling argumentagainst a look-up table is that there is no provision for adecision-making process. And finally, if the circuit parameters changeover time, the look-up table does not provide a mechanism foradaptability. In other words, the values stored in the look-up tablebecome invalid.

In the case of a rule-based system, one of the rules will be if theFrequency is x and the Power is y, then tune z. This means that therules will select the optimum frequency range and adjust the tuner. Forthe Smith chart shown in FIG. 6, this will result in the selection ofthe optimum result in the maximum P_(out). Since the response of theamplifier is slightly inductive, with the real part being close to 50ohms, the tuning solution set is established for that particularfrequency set that is slightly capacitive to bring the imaginary partclose to zero.

Note, the transmitter is in operational mode at the completion of theinitialization phase. It is shown in the diagram that a digitalfeed-forward algorithm will be used for gain adjust. Thus stated, theremainder of this discussion will illustrate the flow chart of thecontrol algorithm. The system continuously monitors P_(out) and F_(in)(power output and frequency input) and compares those values to thoseknown values in the initialization file (even though these values mayhave been updated from the knowledge function). A rule set allows themonitoring of the entire bandwidth from 6 to 18 GHz. Therefore, if thefrequency were to change, the frequency discriminator will sense thechange in frequency and the algorithm will scan all the rules that applyto frequency behavior simultaneously.

More particularly, FIG. 7 shows the flow chart for the operational modeof the transmitter.

In the operational mode of the transmitter, as can be seen at 170, theprocess is started simultaneously monitoring the input power at monitor172, the output power at monitor 174, and the output frequency at 176.With respect to the input power control, the monitored input power iscompared with the input power in the initialization procedure asillustrated at 178. If these two entities are not equal, the amplifieris adjusted at 180 and the result is measured. If the result shows thatthe input power equals the initialized input power as determined at 182,then there is no further gain adjustment. If they are not equal,adjustment is attempted.

With respect to the monitoring of the output power, as far as theimpedance matching algorithm is concerned, the monitored output power iscompared at 184 to the initialized output power and if they are the samethen the process stops.

If the output power does not equal the initialized output power, thenfor the logical evaluation of all outputs, box 190 is called up. It isat this box where the fuzzy logic compares the sensed output power withthe initialized output power.

The result of the evaluation or what could be a comparison step iscoupled to an inference engine 192. It is here at the inference enginethat the root mean square process is initiated to determine a weightcorresponding to the error signals based on how bad the initial measuredcondition was. The inference engine then causes the tuner to re-tune.

The output of the inference engine 192 is applied to a decision engine194. Also inputted into the decision engine is an input from knowledgebase 196 and a learned function 198, the purpose of which is to derive asmarter tuning scenario.

For instance, if one tunes nine times, if every time the transceiver isturned on it goes through this process and knows where a good tune is,then the process is placed in the memory and the results can be used atthe output of the inference engine to effectuate a good tune.

As a result, the output of the inference engine is used to generate thedigital code that is coupled to the tuner for re-tuning.

Note that if the output of the decision engine results in the outputpower equaling the initial output power as illustrated at 200, then nolearning is indicated.

The same processing is true for the monitoring of the input frequency,which is monitored at decision block 202 and with knowledge base 204being updated through a learning process 206 that assists the inferenceengine. Decision engine 194 is again invoked by decision block 208 toascertain if the input frequency is the same as the initialized value.If not, learning process 206 is updated.

The Role of Fuzzy Logic

As can be seen from FIG. 8, a decision table is used to determine whattype of control signal is to be applied back to the tuner of FIG. 1. Itis here that the fuzzy logic comes into play.

In FIG. 8, the decision table, which is an instantiation of a fuzzylogic rule set, maps the decisions against the power and frequencyerrors in the system. The rules follow the fuzzy equations IF x and yThen z format described earlier. The rule “Tune Zero” will not besatisfied until maximum P_(out) is achieved. The degree of frequencyerror is based on the initialization of the normalized 12 GHz response.When the frequency error is positive, then the system will need to tunenegative and conversely if the frequency error is positive, then thesystem will tune negative to achieve the best response. It may seemoversimplified, but this logical table covers the entire spectrum ofinput frequencies, output power, and possible responses. Even though theoutput is not exactly clear at this point, this does form the basis of afuzzy logic system. In the vernacular, this is termed “fuzzification”and is the first step in the inference process. The next step in theinference process is computation of the logical values (more than onemay be valid, even though in this example only one rule was legitimate)and the application of fuzzy math to derive the membership functions,meaning to select a solution set and solve.

Specifically, from FIG. 8, it can be seen, if F_(error) is negative andP_(error) is zero, then according to the tuner code one has a very smallpositive, VPS, correction. If F_(error) is negative and P_(error) issmall, then there is a positive small, PS, tuner code coupled to thetuner. If F_(error) is negative and P_(error) is medium, then the tunercode, PM, indicates a medium positive shift for the tuner. If F_(error)is negative and P_(error) is high, then one needs a positive high tuningshift, PH.

The above discusses the case where F_(error) is negative. Below on thistable is discussed conditions where F_(error) is either zero orpositive, with the indicated tuner codes indicated to shift the tuner inthe presented manner.

How high is high, or how low is small, is based on rules set up by theexpert and offers the type of imprecision afforded by the fuzzy logic.

While the present invention has been described in connection with thepreferred embodiments of the various figures, it is to be understoodthat other similar embodiments may be used or modifications or additionsmay be made to the described embodiment for performing the same functionof the present invention without deviating therefrom. Therefore, thepresent invention should not be limited to any single embodiment, butrather construed in breadth and scope in accordance with the recitationof the appended claims.

1. A method for controlling an RF power amplifier and tuner connectedbetween the RF power amplifier and an antenna using a fuzzy logicalgorithm operating on Smith chart based calculations of the impedancematch between the output of the RF power amplifier and the antenna inputin a closed loop feedback circuit comprising the steps of: sensing thefrequency of the RF signal applied to the RF power amplifier to monitorthe amplifier performance of the RF power amplifier in a continuousmonitoring step; and applying a self-correction tuning that reduces anyimpedance mismatch, the RF power amplifier having an input signalcoupled thereto, the monitoring step including the step of monitoringthe output power of the RF power amplifier and the input frequency ofthe RF input signal applied to the RF power amplifier and utilizing theresults of the sensed output power and the input frequency forcalculating tuner settings for controlling the tuner to provide a bettermatch between the RF power amplifier and the antenna.
 2. The method ofclaim 1, wherein the fuzzy logic algorithm includes a fuzzy logic ruleset, whereby decisions are made in the tuning of the tuner based onfuzzy logic principles.
 3. The method of claim 1, wherein the fuzzylogic algorithm operates from a knowledge base derived from an expertincluding Smith chart calculations.
 4. The method of claim 3, whereinthe tuner is capable of a number of discrete tuning states and in whichthe discrete tuning states cover a combined Smith Chart that specifiesthe impedance match between the output of the amplifier and the antennainput and wherein the knowledge base includes tuning states of thetuner.
 5. The method of claim 4, wherein the tuning states that describethe match between amplifier and antenna are grouped into a number ofsets of tuning states, with tuning states in a set having similarproperties.
 6. The method of claim 5, wherein for a given tuner a set ofideal tuning states resulting in a close to 1:1 SWR is designated. 7.The method of claim 6, wherein each of the tuning states in a non-idealset is described by a weight that includes the distance of a tuningstate to an ideal tuning state and the direction thereof.
 8. The methodof claim 7, and further including a unit for generating an error signalcorresponding to the difference between an output power associated witha point on the Smith Chart within the ideal set and a frequencydifference between the sensed frequency and that associated with thepoint on the Smith Chart associated with the ideal set, the error signalincluding a distance and direction to the ideal point.
 9. The method ofclaim 8, wherein the magnitude and direction of the error signal is usedto set the tuner state such that the resultant impedance point on theSmith Chart is closer to the ideal point.
 10. The method of claim 5,wherein the use of sets of tuning states reduces the amount of data usedby the fuzzy logic algorithm and concomitantly the complexity of thecomputation, thus to minimize battery power requirements.
 11. The methodof claim 1, wherein the amplifier is battery powered and wherein thefuzzy logic control assures that the amplifier is operating in a linearregion, thus to minimize battery drain.